I conduct interdisciplinary research at the intersection of geographic information science, artificial intelligence, spatial data science, and spatial statistics. My goal is to develop trustworthy GeoAI methods that help researchers understand complex geographic and environmental processes while making geospatial models more transparent, reliable, and reproducible. My work integrates spatial modeling, machine learning, remote sensing, and multi-source geospatial data.

My current research focuses on three interconnected areas: (i) developing spatially explicit models that account for spatial heterogeneity and geographic context, (ii) advancing trustworthy GeoAI through explainable AI and uncertainty quantification, and (iii) creating transferable and scalable GeoAI methods that generalize across geographic regions, datasets, and analytical tasks. I also explore multi-agent GeoAI and the use of large language models to support geospatial model discovery and spatial problem-solving.